Day_3-6 Heart failure Data
Divyosmi Goswami
4/5/2021
Welcome
Hello Dear friends I welcome you to this Eda Notebook. This explores the heart faliure clinical data records dataset. it is gonna be fun. i hope you will enjoy using this notebook. this notebook is dedicated to my day 3-4 of my 100 days of code.
Load Libraries
I will only need tidyverse for this time. Guess what I wil also need corrplot.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.0 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(corrplot) ## corrplot 0.84 loadedlibrary()Data exploration
Let us dig up the data and explore it. Oh and yes 1 good news it is a totally cleaned data. Data set link (link)[https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv]
x <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv"))
x## age anaemia creatinine_phosphokinase diabetes ejection_fraction
## 1 75.000 0 582 0 20
## 2 55.000 0 7861 0 38
## 3 65.000 0 146 0 20
## 4 50.000 1 111 0 20
## 5 65.000 1 160 1 20
## 6 90.000 1 47 0 40
## 7 75.000 1 246 0 15
## 8 60.000 1 315 1 60
## 9 65.000 0 157 0 65
## 10 80.000 1 123 0 35
## 11 75.000 1 81 0 38
## 12 62.000 0 231 0 25
## 13 45.000 1 981 0 30
## 14 50.000 1 168 0 38
## 15 49.000 1 80 0 30
## 16 82.000 1 379 0 50
## 17 87.000 1 149 0 38
## 18 45.000 0 582 0 14
## 19 70.000 1 125 0 25
## 20 48.000 1 582 1 55
## 21 65.000 1 52 0 25
## 22 65.000 1 128 1 30
## 23 68.000 1 220 0 35
## 24 53.000 0 63 1 60
## 25 75.000 0 582 1 30
## 26 80.000 0 148 1 38
## 27 95.000 1 112 0 40
## 28 70.000 0 122 1 45
## 29 58.000 1 60 0 38
## 30 82.000 0 70 1 30
## 31 94.000 0 582 1 38
## 32 85.000 0 23 0 45
## 33 50.000 1 249 1 35
## 34 50.000 1 159 1 30
## 35 65.000 0 94 1 50
## 36 69.000 0 582 1 35
## 37 90.000 1 60 1 50
## 38 82.000 1 855 1 50
## 39 60.000 0 2656 1 30
## 40 60.000 0 235 1 38
## 41 70.000 0 582 0 20
## 42 50.000 0 124 1 30
## 43 70.000 0 571 1 45
## 44 72.000 0 127 1 50
## 45 60.000 1 588 1 60
## 46 50.000 0 582 1 38
## 47 51.000 0 1380 0 25
## 48 60.000 0 582 1 38
## 49 80.000 1 553 0 20
## 50 57.000 1 129 0 30
## 51 68.000 1 577 0 25
## 52 53.000 1 91 0 20
## 53 60.000 0 3964 1 62
## 54 70.000 1 69 1 50
## 55 60.000 1 260 1 38
## 56 95.000 1 371 0 30
## 57 70.000 1 75 0 35
## 58 60.000 1 607 0 40
## 59 49.000 0 789 0 20
## 60 72.000 0 364 1 20
## 61 45.000 0 7702 1 25
## 62 50.000 0 318 0 40
## 63 55.000 0 109 0 35
## 64 45.000 0 582 0 35
## 65 45.000 0 582 0 80
## 66 60.000 0 68 0 20
## 67 42.000 1 250 1 15
## 68 72.000 1 110 0 25
## 69 70.000 0 161 0 25
## 70 65.000 0 113 1 25
## 71 41.000 0 148 0 40
## 72 58.000 0 582 1 35
## 73 85.000 0 5882 0 35
## 74 65.000 0 224 1 50
## 75 69.000 0 582 0 20
## 76 60.000 1 47 0 20
## 77 70.000 0 92 0 60
## 78 42.000 0 102 1 40
## 79 75.000 1 203 1 38
## 80 55.000 0 336 0 45
## 81 70.000 0 69 0 40
## 82 67.000 0 582 0 50
## 83 60.000 1 76 1 25
## 84 79.000 1 55 0 50
## 85 59.000 1 280 1 25
## 86 51.000 0 78 0 50
## 87 55.000 0 47 0 35
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## 90 57.000 1 115 0 25
## 91 70.000 0 66 1 45
## 92 60.000 0 897 1 45
## 93 42.000 0 582 0 60
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## 95 58.000 0 144 1 38
## 96 58.000 1 133 0 60
## 97 63.000 1 514 1 25
## 98 70.000 1 59 0 60
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## 100 63.000 1 61 1 40
## 101 65.000 1 305 0 25
## 102 75.000 0 582 0 45
## 103 80.000 0 898 0 25
## 104 42.000 0 5209 0 30
## 105 60.000 0 53 0 50
## 106 72.000 1 328 0 30
## 107 55.000 0 748 0 45
## 108 45.000 1 1876 1 35
## 109 63.000 0 936 0 38
## 110 45.000 0 292 1 35
## 111 85.000 0 129 0 60
## 112 55.000 0 60 0 35
## 113 50.000 0 369 1 25
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## 116 58.000 1 400 0 40
## 117 60.000 1 96 1 60
## 118 85.000 1 102 0 60
## 119 65.000 1 113 1 60
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## 122 66.000 1 68 1 38
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## 124 60.000 1 582 0 30
## 125 60.000 0 582 0 40
## 126 43.000 1 358 0 50
## 127 46.000 0 168 1 17
## 128 58.000 1 200 1 60
## 129 61.000 0 248 0 30
## 130 53.000 1 270 1 35
## 131 53.000 1 1808 0 60
## 132 60.000 1 1082 1 45
## 133 46.000 0 719 0 40
## 134 63.000 0 193 0 60
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## 137 65.000 1 59 1 60
## 138 68.000 1 646 0 25
## 139 62.000 0 281 1 35
## 140 50.000 0 1548 0 30
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## 148 64.000 0 1610 0 60
## 149 75.000 1 582 0 30
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## 152 62.000 0 30 1 60
## 153 50.000 0 115 0 45
## 154 50.000 0 1846 1 35
## 155 65.000 1 335 0 35
## 156 60.000 1 231 1 25
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## 159 85.000 1 910 0 50
## 160 59.000 1 129 0 45
## 161 66.000 1 72 0 40
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## 164 50.000 1 2334 1 35
## 165 45.000 0 2442 1 30
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## 169 65.000 0 582 1 40
## 170 70.000 0 835 0 35
## 171 51.000 1 582 1 35
## 172 52.000 0 3966 0 40
## 173 70.000 1 171 0 60
## 174 50.000 1 115 0 20
## 175 65.000 0 198 1 35
## 176 60.000 1 95 0 60
## 177 69.000 0 1419 0 40
## 178 49.000 1 69 0 50
## 179 63.000 1 122 1 60
## 180 55.000 0 835 0 40
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## 182 59.000 1 176 1 25
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## 185 58.000 1 145 0 25
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## 221 73.000 0 582 0 20
## 222 65.000 0 118 0 50
## 223 42.000 1 86 0 35
## 224 47.000 0 582 0 25
## 225 58.000 0 582 1 25
## 226 75.000 0 675 1 60
## 227 58.000 1 57 0 25
## 228 55.000 1 2794 0 35
## 229 65.000 0 56 0 25
## 230 72.000 0 211 0 25
## 231 60.000 0 166 0 30
## 232 70.000 0 93 0 35
## 233 40.000 1 129 0 35
## 234 53.000 1 707 0 38
## 235 53.000 1 582 0 45
## 236 77.000 1 109 0 50
## 237 75.000 0 119 0 50
## 238 70.000 0 232 0 30
## 239 65.000 1 720 1 40
## 240 55.000 1 180 0 45
## 241 70.000 0 81 1 35
## 242 65.000 0 582 1 30
## 243 40.000 0 90 0 35
## 244 73.000 1 1185 0 40
## 245 54.000 0 582 1 38
## 246 61.000 1 80 1 38
## 247 55.000 0 2017 0 25
## 248 64.000 0 143 0 25
## 249 40.000 0 624 0 35
## 250 53.000 0 207 1 40
## 251 50.000 0 2522 0 30
## 252 55.000 0 572 1 35
## 253 50.000 0 245 0 45
## 254 70.000 0 88 1 35
## 255 53.000 1 446 0 60
## 256 52.000 1 191 1 30
## 257 65.000 0 326 0 38
## 258 58.000 0 132 1 38
## 259 45.000 1 66 1 25
## 260 53.000 0 56 0 50
## 261 55.000 0 66 0 40
## 262 62.000 1 655 0 40
## 263 65.000 1 258 1 25
## 264 68.000 1 157 1 60
## 265 61.000 0 582 1 38
## 266 50.000 1 298 0 35
## 267 55.000 0 1199 0 20
## 268 56.000 1 135 1 38
## 269 45.000 0 582 1 38
## 270 40.000 0 582 1 35
## 271 44.000 0 582 1 30
## 272 51.000 0 582 1 40
## 273 67.000 0 213 0 38
## 274 42.000 0 64 0 40
## 275 60.000 1 257 1 30
## 276 45.000 0 582 0 38
## 277 70.000 0 618 0 35
## 278 70.000 0 582 1 38
## 279 50.000 1 1051 1 30
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## 283 42.000 0 64 0 30
## 284 65.000 0 1688 0 38
## 285 50.000 1 54 0 40
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## 294 63.000 1 103 1 35
## 295 62.000 0 61 1 38
## 296 55.000 0 1820 0 38
## 297 45.000 0 2060 1 60
## 298 45.000 0 2413 0 38
## 299 50.000 0 196 0 45
## high_blood_pressure platelets serum_creatinine serum_sodium sex smoking
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## 4 0 210000 1.90 137 1 0
## 5 0 327000 2.70 116 0 0
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## 15 1 427000 1.00 138 0 0
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## 17 0 262000 0.90 140 1 0
## 18 0 166000 0.80 127 1 0
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## time DEATH_EVENT
## 1 4 1
## 2 6 1
## 3 7 1
## 4 7 1
## 5 8 1
## 6 8 1
## 7 10 1
## 8 10 1
## 9 10 1
## 10 10 1
## 11 10 1
## 12 10 1
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## 14 11 1
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## 25 23 1
## 26 23 1
## 27 24 1
## 28 26 1
## 29 26 1
## 30 26 1
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## 36 30 1
## 37 30 1
## 38 30 1
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## 40 30 1
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## 42 32 1
## 43 33 1
## 44 33 0
## 45 33 1
## 46 35 1
## 47 38 1
## 48 40 1
## 49 41 1
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## 51 43 1
## 52 43 1
## 53 43 1
## 54 44 1
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## 56 50 1
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## 65 63 0
## 66 64 1
## 67 65 1
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## 178 147 0
## 179 147 0
## 180 147 0
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## 182 150 1
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## 186 171 1
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## 190 174 0
## 191 174 0
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## 195 180 1
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## 198 186 0
## 199 186 0
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## 205 187 0
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## 209 187 0
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## 245 213 0
## 246 213 0
## 247 214 1
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## 253 215 0
## 254 215 0
## 255 215 0
## 256 216 0
## 257 220 0
## 258 230 0
## 259 230 0
## 260 231 0
## 261 233 0
## 262 233 0
## 263 235 1
## 264 237 0
## 265 237 0
## 266 240 0
## 267 241 1
## 268 244 0
## 269 244 0
## 270 244 0
## 271 244 0
## 272 244 0
## 273 245 0
## 274 245 0
## 275 245 0
## 276 245 0
## 277 245 0
## 278 246 0
## 279 246 0
## 280 246 0
## 281 247 0
## 282 250 0
## 283 250 0
## 284 250 0
## 285 250 0
## 286 250 0
## 287 250 0
## 288 250 0
## 289 256 0
## 290 256 0
## 291 257 0
## 292 258 0
## 293 258 0
## 294 270 0
## 295 270 0
## 296 271 0
## 297 278 0
## 298 280 0
## 299 285 0dim(x)## [1] 299 13summary(x)## age anaemia creatinine_phosphokinase diabetes
## Min. :40.00 Min. :0.0000 Min. : 23.0 Min. :0.0000
## 1st Qu.:51.00 1st Qu.:0.0000 1st Qu.: 116.5 1st Qu.:0.0000
## Median :60.00 Median :0.0000 Median : 250.0 Median :0.0000
## Mean :60.83 Mean :0.4314 Mean : 581.8 Mean :0.4181
## 3rd Qu.:70.00 3rd Qu.:1.0000 3rd Qu.: 582.0 3rd Qu.:1.0000
## Max. :95.00 Max. :1.0000 Max. :7861.0 Max. :1.0000
## ejection_fraction high_blood_pressure platelets serum_creatinine
## Min. :14.00 Min. :0.0000 Min. : 25100 Min. :0.500
## 1st Qu.:30.00 1st Qu.:0.0000 1st Qu.:212500 1st Qu.:0.900
## Median :38.00 Median :0.0000 Median :262000 Median :1.100
## Mean :38.08 Mean :0.3512 Mean :263358 Mean :1.394
## 3rd Qu.:45.00 3rd Qu.:1.0000 3rd Qu.:303500 3rd Qu.:1.400
## Max. :80.00 Max. :1.0000 Max. :850000 Max. :9.400
## serum_sodium sex smoking time
## Min. :113.0 Min. :0.0000 Min. :0.0000 Min. : 4.0
## 1st Qu.:134.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 73.0
## Median :137.0 Median :1.0000 Median :0.0000 Median :115.0
## Mean :136.6 Mean :0.6488 Mean :0.3211 Mean :130.3
## 3rd Qu.:140.0 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:203.0
## Max. :148.0 Max. :1.0000 Max. :1.0000 Max. :285.0
## DEATH_EVENT
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.3211
## 3rd Qu.:1.0000
## Max. :1.0000nrow(x)## [1] 299colnames(x)## [1] "age" "anaemia"
## [3] "creatinine_phosphokinase" "diabetes"
## [5] "ejection_fraction" "high_blood_pressure"
## [7] "platelets" "serum_creatinine"
## [9] "serum_sodium" "sex"
## [11] "smoking" "time"
## [13] "DEATH_EVENT"str(x)## 'data.frame': 299 obs. of 13 variables:
## $ age : num 75 55 65 50 65 90 75 60 65 80 ...
## $ anaemia : int 0 0 0 1 1 1 1 1 0 1 ...
## $ creatinine_phosphokinase: int 582 7861 146 111 160 47 246 315 157 123 ...
## $ diabetes : int 0 0 0 0 1 0 0 1 0 0 ...
## $ ejection_fraction : int 20 38 20 20 20 40 15 60 65 35 ...
## $ high_blood_pressure : int 1 0 0 0 0 1 0 0 0 1 ...
## $ platelets : num 265000 263358 162000 210000 327000 ...
## $ serum_creatinine : num 1.9 1.1 1.3 1.9 2.7 2.1 1.2 1.1 1.5 9.4 ...
## $ serum_sodium : int 130 136 129 137 116 132 137 131 138 133 ...
## $ sex : int 1 1 1 1 0 1 1 1 0 1 ...
## $ smoking : int 0 0 1 0 0 1 0 1 0 1 ...
## $ time : int 4 6 7 7 8 8 10 10 10 10 ...
## $ DEATH_EVENT : int 1 1 1 1 1 1 1 1 1 1 ...visualization
It is day 4 and I still have modelling to do. Hope to finish soon. Feel like writing hello world and telling see i did hello world. fun fact i never wrote hello world as the first program i remember writing 2+2 in python intepreter.
vizCol = function (x){
c = 1
for(i in x){
if(length(unique(i)) == 2){
pie(table(i),main=colnames(x)[c])
}
else{
hist(i,main= colnames(x)[c],xlab= colnames(x)[c])
}
c = c+1
}
}
vizCol(x)
cor(x, method = c("pearson"))## age anaemia creatinine_phosphokinase
## age 1.00000000 0.08800644 -0.081583900
## anaemia 0.08800644 1.00000000 -0.190741030
## creatinine_phosphokinase -0.08158390 -0.19074103 1.000000000
## diabetes -0.10101239 -0.01272905 -0.009638514
## ejection_fraction 0.06009836 0.03155697 -0.044079554
## high_blood_pressure 0.09328868 0.03818200 -0.070589980
## platelets -0.05235437 -0.04378555 0.024463389
## serum_creatinine 0.15918713 0.05217360 -0.016408480
## serum_sodium -0.04596584 0.04188161 0.059550156
## sex 0.06542952 -0.09476896 0.079790629
## smoking 0.01866787 -0.10728984 0.002421235
## time -0.22406842 -0.14141398 -0.009345653
## DEATH_EVENT 0.25372854 0.06627010 0.062728160
## diabetes ejection_fraction high_blood_pressure
## age -0.101012385 0.06009836 0.093288685
## anaemia -0.012729046 0.03155697 0.038182003
## creatinine_phosphokinase -0.009638514 -0.04407955 -0.070589980
## diabetes 1.000000000 -0.00485031 -0.012732382
## ejection_fraction -0.004850310 1.00000000 0.024444731
## high_blood_pressure -0.012732382 0.02444473 1.000000000
## platelets 0.092192828 0.07217747 0.049963481
## serum_creatinine -0.046975315 -0.01130247 -0.004934525
## serum_sodium -0.089550619 0.17590228 0.037109470
## sex -0.157729504 -0.14838597 -0.104614629
## smoking -0.147173413 -0.06731457 -0.055711369
## time 0.033725509 0.04172924 -0.196439479
## DEATH_EVENT -0.001942883 -0.26860331 0.079351058
## platelets serum_creatinine serum_sodium sex
## age -0.05235437 0.159187133 -0.045965841 0.065429524
## anaemia -0.04378555 0.052173604 0.041881610 -0.094768961
## creatinine_phosphokinase 0.02446339 -0.016408480 0.059550156 0.079790629
## diabetes 0.09219283 -0.046975315 -0.089550619 -0.157729504
## ejection_fraction 0.07217747 -0.011302475 0.175902282 -0.148385965
## high_blood_pressure 0.04996348 -0.004934525 0.037109470 -0.104614629
## platelets 1.00000000 -0.041198077 0.062124619 -0.125120483
## serum_creatinine -0.04119808 1.000000000 -0.189095210 0.006969778
## serum_sodium 0.06212462 -0.189095210 1.000000000 -0.027566123
## sex -0.12512048 0.006969778 -0.027566123 1.000000000
## smoking 0.02823445 -0.027414135 0.004813195 0.445891712
## time 0.01051391 -0.149315418 0.087640000 -0.015608220
## DEATH_EVENT -0.04913887 0.294277561 -0.195203596 -0.004316376
## smoking time DEATH_EVENT
## age 0.018667868 -0.224068420 0.253728543
## anaemia -0.107289838 -0.141413982 0.066270098
## creatinine_phosphokinase 0.002421235 -0.009345653 0.062728160
## diabetes -0.147173413 0.033725509 -0.001942883
## ejection_fraction -0.067314567 0.041729235 -0.268603312
## high_blood_pressure -0.055711369 -0.196439479 0.079351058
## platelets 0.028234448 0.010513909 -0.049138868
## serum_creatinine -0.027414135 -0.149315418 0.294277561
## serum_sodium 0.004813195 0.087640000 -0.195203596
## sex 0.445891712 -0.015608220 -0.004316376
## smoking 1.000000000 -0.022838942 -0.012623153
## time -0.022838942 1.000000000 -0.526963779
## DEATH_EVENT -0.012623153 -0.526963779 1.000000000corrplot(cor(x, method = c("pearson")))
cor(x, method = c("kendall"))## age anaemia creatinine_phosphokinase
## age 1.00000000 0.06018952 -0.06317984
## anaemia 0.06018952 1.00000000 -0.17871152
## creatinine_phosphokinase -0.06317984 -0.17871152 1.00000000
## diabetes -0.07502884 -0.01272905 0.03498517
## ejection_fraction 0.05128355 0.01751860 -0.04706064
## high_blood_pressure 0.08848751 0.03818200 -0.06675289
## platelets -0.03719046 -0.02925926 0.03974166
## serum_creatinine 0.19008853 -0.01377362 -0.03513914
## serum_sodium -0.07336805 0.05211503 0.01312920
## sex 0.04836183 -0.09476896 0.01099657
## smoking 0.02522659 -0.10728984 -0.02509830
## time -0.13666022 -0.11049803 0.08344361
## DEATH_EVENT 0.18253612 0.06627010 0.01952854
## diabetes ejection_fraction high_blood_pressure
## age -0.075028842 0.05128355 0.08848751
## anaemia -0.012729046 0.01751860 0.03818200
## creatinine_phosphokinase 0.034985166 -0.04706064 -0.06675289
## diabetes 1.000000000 -0.01024153 -0.01273238
## ejection_fraction -0.010241535 1.00000000 0.01543466
## high_blood_pressure -0.012732382 0.01543466 1.00000000
## platelets 0.060665037 0.03787030 0.05671458
## serum_creatinine 0.001636943 -0.12974458 -0.07594416
## serum_sodium -0.047006815 0.11826139 0.01636398
## sex -0.157729504 -0.11250780 -0.10461463
## smoking -0.147173413 -0.06171843 -0.05571137
## time 0.024208802 0.05090795 -0.15985206
## DEATH_EVENT -0.001942883 -0.24676587 0.07935106
## platelets serum_creatinine serum_sodium
## age -0.037190457 0.190088533 -0.073368050
## anaemia -0.029259264 -0.013773624 0.052115032
## creatinine_phosphokinase 0.039741660 -0.035139139 0.013129199
## diabetes 0.060665037 0.001636943 -0.047006815
## ejection_fraction 0.037870302 -0.129744577 0.118261387
## high_blood_pressure 0.056714580 -0.075944158 0.016363981
## platelets 1.000000000 -0.035681059 0.034560658
## serum_creatinine -0.035681059 1.000000000 -0.223006634
## serum_sodium 0.034560658 -0.223006634 1.000000000
## sex -0.112928738 0.044185692 -0.065421329
## smoking 0.003922433 -0.014434671 0.007074076
## time -0.004368795 -0.109593108 0.059389116
## DEATH_EVENT -0.037962329 0.313821738 -0.178089858
## sex smoking time DEATH_EVENT
## age 0.048361829 0.025226589 -0.136660217 0.182536115
## anaemia -0.094768961 -0.107289838 -0.110498032 0.066270098
## creatinine_phosphokinase 0.010996573 -0.025098301 0.083443605 0.019528541
## diabetes -0.157729504 -0.147173413 0.024208802 -0.001942883
## ejection_fraction -0.112507804 -0.061718426 0.050907948 -0.246765869
## high_blood_pressure -0.104614629 -0.055711369 -0.159852057 0.079351058
## platelets -0.112928738 0.003922433 -0.004368795 -0.037962329
## serum_creatinine 0.044185692 -0.014434671 -0.109593108 0.313821738
## serum_sodium -0.065421329 0.007074076 0.059389116 -0.178089858
## sex 1.000000000 0.445891712 -0.014223136 -0.004316376
## smoking 0.445891712 1.000000000 -0.021727014 -0.012623153
## time -0.014223136 -0.021727014 1.000000000 -0.445744327
## DEATH_EVENT -0.004316376 -0.012623153 -0.445744327 1.000000000corrplot(cor(x, method = c("kendall")))
cor(x, method = c("spearman"))## age anaemia creatinine_phosphokinase
## age 1.00000000 0.07192447 -0.09307820
## anaemia 0.07192447 1.00000000 -0.21611641
## creatinine_phosphokinase -0.09307820 -0.21611641 1.00000000
## diabetes -0.08965696 -0.01272905 0.04230767
## ejection_fraction 0.07404712 0.02036560 -0.06783851
## high_blood_pressure 0.10573962 0.03818200 -0.08072449
## platelets -0.05209077 -0.03560822 0.06003328
## serum_creatinine 0.27056117 -0.01626694 -0.04993642
## serum_sodium -0.10171585 0.06140528 0.01688573
## sex 0.05779077 -0.09476896 0.01329819
## smoking 0.03014493 -0.10728984 -0.03035146
## time -0.19752448 -0.13465158 0.12582268
## DEATH_EVENT 0.21812457 0.06627010 0.02361593
## diabetes ejection_fraction high_blood_pressure
## age -0.089656962 0.07404712 0.10573962
## anaemia -0.012729046 0.02036560 0.03818200
## creatinine_phosphokinase 0.042307673 -0.06783851 -0.08072449
## diabetes 1.000000000 -0.01190592 -0.01273238
## ejection_fraction -0.011905918 1.00000000 0.01794300
## high_blood_pressure -0.012732382 0.01794300 1.00000000
## platelets 0.073828720 0.05369565 0.06902106
## serum_creatinine 0.001933264 -0.17798621 -0.08969164
## serum_sodium -0.055386448 0.16169539 0.01928109
## sex -0.157729504 -0.13079179 -0.10461463
## smoking -0.147173413 -0.07174847 -0.05571137
## time 0.029500556 0.07053340 -0.19479380
## DEATH_EVENT -0.001942883 -0.28686853 0.07935106
## platelets serum_creatinine serum_sodium
## age -0.052090768 0.270561175 -0.10171585
## anaemia -0.035608220 -0.016266939 0.06140528
## creatinine_phosphokinase 0.060033280 -0.049936417 0.01688573
## diabetes 0.073828720 0.001933264 -0.05538645
## ejection_fraction 0.053695653 -0.177986205 0.16169539
## high_blood_pressure 0.069021056 -0.089691640 0.01928109
## platelets 1.000000000 -0.051027718 0.04945255
## serum_creatinine -0.051027718 1.000000000 -0.30041326
## serum_sodium 0.049452551 -0.300413264 1.00000000
## sex -0.137433102 0.052184227 -0.07708361
## smoking 0.004773560 -0.017047648 0.00833513
## time -0.006927269 -0.160989690 0.08639338
## DEATH_EVENT -0.046199760 0.370630041 -0.20983691
## sex smoking time DEATH_EVENT
## age 0.057790771 0.03014493 -0.197524485 0.218124565
## anaemia -0.094768961 -0.10728984 -0.134651578 0.066270098
## creatinine_phosphokinase 0.013298190 -0.03035146 0.125822682 0.023615927
## diabetes -0.157729504 -0.14717341 0.029500556 -0.001942883
## ejection_fraction -0.130791786 -0.07174847 0.070533396 -0.286868533
## high_blood_pressure -0.104614629 -0.05571137 -0.194793801 0.079351058
## platelets -0.137433102 0.00477356 -0.006927269 -0.046199760
## serum_creatinine 0.052184227 -0.01704765 -0.160989690 0.370630041
## serum_sodium -0.077083612 0.00833513 0.086393376 -0.209836909
## sex 1.000000000 0.44589171 -0.017332143 -0.004316376
## smoking 0.445891712 1.00000000 -0.026476278 -0.012623153
## time -0.017332143 -0.02647628 1.000000000 -0.543178696
## DEATH_EVENT -0.004316376 -0.01262315 -0.543178696 1.000000000corrplot(cor(x, method = c("spearman")))
I love experimenting and so does this interact function had thought about creating a better thing but vizCol already did that for me so it was interact-ive experiment time.
interact = function (x){
for(i in x){
for(j in x){
plot(i,j)
}
}
}
interact(x)
#### Thank you for using.